The skills that matter in the age of AI for hiring and career growth

I was recently working with a chief technology officer who was hiring an engineer for his team. During the job briefing call he said something I wasn't expecting.

"Georgie, when it comes to technical ability I need somebody who's around a five out of 10. AI can now handle much of the grunt work. But when it comes to human skills I need somebody who's a nine."

I remember that conversation vividly because it changed everything I had been recruiting for up until that point.

This was a CTO, hiring an engineer, telling me that technical ability was now the lesser requirement. That's not a throwaway comment. That's a signal, and if you're leading a team, hiring for one, or managing your own career in the age of AI, it's a signal worth paying attention to.

Because here's what I'm seeing after 12 years in technology recruitment: the future of work isn't going to be won on credentials, job titles or education. The competitive advantage in AI-era roles is emerging from integrating AI fluency with elevated human judgement, adaptability, curiosity, leadership and emotional intelligence.

Why employers are shifting from credentials to capabilities

A decade ago technology hiring briefs were predictable. Specific degrees, years of experience in a narrowly defined stack. Tenure at recognisable organisations and linear career progression is what stood out. 

That formula is breaking down.

Recruiting across cloud, engineering, product, AI and digital transformation, the opening question from hiring managers has changed. It's no longer ‘do they have the right degree?’ It's:

• Can this person navigate ambiguity?
• How do they learn in fast-moving environments?
• Can they influence without authority?
• How well do they work alongside AI tools?

The World Economic Forum’s Future of Jobs Report 2025 shows that analytical thinking, resilience, flexibility and leadership are among the core skills employers say they need most now and through 2030, with nearly 40% of workers’ core skills expected to change in that period. Recent LinkedIn labour market insights (2026) reflect the same pattern playing out in real time, with required skills evolving rapidly and employers placing increasing weight on capability rather than credentials.

A hiring manager I worked with recently on a senior data role told me, "I need problem solvers, not certificates."

Why AI fluency is now a baseline skill

From where I sit AI fluency is becoming as foundational as digital literacy was 20 years ago. It doesn't mean everyone needs to build large language models. It means professionals need to understand:

• What AI tools can and can't do
• How to prompt, iterate and evaluate outputs
• Where human judgement must step in
• How automation is reshaping workflows and risk

McKinsey estimates that generative AI could automate activities absorbing 60-70% of employees' time, not entire roles but significant chunks of how work currently gets done. The OECD (Organisation for Economic Co-operation and Development) has noted this is more about task redesign than job elimination.

In hiring conversations this is already showing up. Hiring managers are asking:

• How have you used AI to increase your productivity?
• Where have you redesigned a process using automation?
• How do you ensure quality when working with AI-generated outputs?

Candidates who can answer these questions thoughtfully signal adaptability. Those who avoid the topic signal they're standing still.

Why human skills are being weighted more heavily

In a recent recruitment search for a head of platform engineering two final-round candidates had near-identical technical profiles. The deciding factor wasn't code quality. It was their ability to translate complex architecture into board-level language, coach teams through change and make decisions under uncertainty.

The technical threshold was assumed, but what separated them was judgement.

In AI-enabled environments leaders have to navigate ethical ambiguity, data governance trade-offs, rapid experimentation cycles, and collaboration between teams who think very differently. Those aren't technical challenges, they're human ones.

What’s changed is not that technical skills are irrelevant. It’s that technical execution is no longer the scarce resource.

AI tools are accelerating coding, automating analysis and reducing the time it takes to produce technical outputs. 

The real constraint inside organisations is no longer ‘can we build it?’ but ‘should we build it?’, ‘how do we apply it responsibly?’ and ‘how do we embed it into workflows without creating risk?’

That is why some of the fastest-growing AI-era roles are not pure model builders. They are AI product leaders, implementation specialists, governance leads, transformation directors and operational integrators. The real value now sits where technical fluency meets commercial judgement.

What I'm seeing in hiring briefs

Three patterns are showing up consistently across the briefs I'm working on:

Skills over tenure. Hiring managers are less interested in years in role and more interested in demonstrable outcomes. Portfolios, case studies and impact narratives are carrying real weight, but time served, much less so.

Breadth over narrow specialism. Deep specialists are still valued but organisations increasingly want T-shaped professionals. Depth in one area and the ability to collaborate across disciplines.

Communication as a strategic skill. Candidates who can influence stakeholders, manage risk conversations and lead change are being fast-tracked. The ability to translate complexity into clarity is no longer a nice-to-have.

The gender problem nobody's talking about

Here's something that doesn't get enough airtime in these conversations: the way we write and advertise roles is quietly filtering out some of the most capable candidates before they ever apply.

Research has consistently shown that women tend to apply for roles only when they meet close to 100% of the listed criteria. Men apply at around 60%. That gap isn't a confidence problem to be fixed on the women's side, it's a job description problem on the organisation's side and it matters enormously right now.

We’re redesigning roles for an AI-first world. We’re moving away from rigid technical checklists toward skills, potential and adaptability. Yet if our job advertisements still read like exhaustive wish lists, we risk narrowing our talent pools and unintentionally filtering out capable professionals, including many women, who may not meet every line item but have the adaptability and judgement these roles now demand.

Experienced, capable, high-judgement professionals who are learning fast and bringing hard-won human skills to the table. Many of them are mid-career women.

If organisations are serious about building AI-era teams that integrate technical fluency with human capability, job advertisement design has to be part of the conversation. That means:

• Separating must-haves from nice-to-haves and being honest about which is which
• Writing to the role's actual outcomes, not an idealised candidate profile
• Reviewing language for exclusionary patterns 
• Actively signalling that breadth of experience, career transitions and non-linear paths are welcome

The talent is there. The question is whether your hiring process is designed to find it or accidentally designed to screen it out.

How HR and people leaders can hire for AI-era skills

If you're looking to operationalise this shift here's where I'd start:

• Embed AI fluency across functions, not just tech teams
  Give managers, HR business partners and commercial leaders baseline education. This isn't a technical team problem, it's an everyone problem.
• Redesign job descriptions
  Remove rigid tenure requirements and degree filters where they don't add value. Prioritise demonstrable skills, outcomes and learning agility, and pay attention to how you're writing them. If your job ad reads like a wishlist you're already narrowing your pipeline before a single application comes in.
• Build internal mobility pathways 
  Encourage lateral moves that build real breadth. In the age of AI resilience comes from understanding how different parts of the business connect, not just climbing one vertical ladder

What this means for your career in the age of AI

For professionals looking to stay in demand in the age of AI here are five things that will help you stand out from what I'm hearing across hundreds of conversations with hiring managers and candidates:

• Develop practical AI fluency. Experiment with tools relevant to your function. Be able to articulate clearly how they improve your productivity or output. Don't wait until it's urgent.
• Document impact, not just activity. Hiring managers want evidence of outcomes and decision-making, not task lists. 
• Strengthen your communication capability. The ability to translate complexity into clarity is increasingly valuable at every level.
• Invest in adaptability. Seek out stretch projects that require real-time learning. Demonstrate comfort with ambiguity; this is now a competitive differentiator.
• Build ethical awareness. Understand data governance, bias and responsible AI considerations in your domain. Leaders who can navigate the ethical dimensions of AI will be trusted more and promoted faster.

Career resilience belongs to those who integrate with AI and continue to build their human skills.